# Human Motion Trajectory Prediction: A Survey

## TL;DR

This survey reviews current methods for human motion trajectory prediction, categorizing approaches, datasets, and metrics, and discusses limitations and future research directions in autonomous systems and surveillance.

## Contribution

It provides a comprehensive taxonomy of existing methods, analyzes datasets and metrics, and highlights research gaps in human motion trajectory prediction.

## Key findings

- Proposed taxonomy categorizes methods by motion modeling and context use.
- Analyzed datasets and performance metrics for trajectory prediction.
- Identified limitations and future research directions in the field.

## Abstract

With growing numbers of intelligent autonomous systems in human environments, the ability of such systems to perceive, understand and anticipate human behavior becomes increasingly important. Specifically, predicting future positions of dynamic agents and planning considering such predictions are key tasks for self-driving vehicles, service robots and advanced surveillance systems. This paper provides a survey of human motion trajectory prediction. We review, analyze and structure a large selection of work from different communities and propose a taxonomy that categorizes existing methods based on the motion modeling approach and level of contextual information used. We provide an overview of the existing datasets and performance metrics. We discuss limitations of the state of the art and outline directions for further research.